Enhancing Situation Awareness for Safe Maritime Navigation: Towards using High-Resolution Electronic Navigational Charts in Highly Automated Shipping

Abstract

Highly automated vessel operations depend on accurate and reliable information about the vessel's surroundings. This paper addresses the challenges of ensuring maritime navigational safety, even when the integrity of Electronic Navigational Charts (ENC) is compromised and nearby obstacles become a collision risk. We propose High-Resolution Electronic Navigational Charts (HR-ENCs) as a tool to capture sensor data, combining LiDAR, radar, camera, GNSS and existing ENCs, using a loosely-coupled sensor fusion framework to assess current conditions within harbors. By accurately identifying and georeferencing maritime objects, we aim to enhance Situation Awareness (SA) and ensure safe navigation of both manned and unmanned vessels. By focusing on the detection of discrepancies between observed conditions and charted objects, HR-ENCs reflect current harbor conditions even when official charts are deprecated. Further, we outline the infrastructure needed to distribute HR-ENCs among maritime stakeholders, showcasing the potential of maintaining harbor-wide charts through consistent and timely updates that are available to all stakeholders. Our approach is applied to a use case in a harbor area. Therefore, we record sensor data with a research vessel and generate 3D data to compute the HR-ENCs. The results confirm the feasibility of integrating 3D measurements into navigational charts. In addition, we explore applications and challenges in using HR-ENCs beyond ENC verification, emphasizing their potential in developing Advanced Driver Assistance Systems (ADAS) for collision avoidance and precise berthing maneuvers. HR-ENCs promise to improve navigational safety, particularly in complex harbor environments, and support the advancement of highly automated maritime systems, thereby contributing to the broader adoption of autonomous maritime operations.